A person who takes 45 minutes to complete the proofreading task made 5 errors. The regression equation predicts the number of mistakes they are expected to make is 4. The residual value will be closest to:

To calculate the residual value, we use the following formula:

\[
\text{Residual} = \text{Actual value} - \text{Predicted value}
\]

Here:
- The actual value (number of mistakes made) = 5
- The predicted value (from the regression equation) = 4

So the residual value is:

\[
\text{Residual} = 5 - 4 = 1
\]

The residual value is **1**.

Would you like more details or have any questions?

Here are five related questions:
1. What is a residual value in the context of regression analysis?
2. How does the residual value affect the accuracy of a regression model?
3. Can a residual value be negative, and what would that indicate?
4. How do you interpret the meaning of a small versus large residual value?
5. What steps can be taken to reduce residuals in a regression model?

**Tip:** Residuals help assess the accuracy of a regression model; smaller residuals generally indicate a better fit.

Explore how residuals are calculated in regression analysis with a detailed example. Understand the concept of residuals and their importance in assessing the accuracy of regression models. Includes a step-by-step solution to a problem where a person makes 5 errors despite a prediction of 4 errors.

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